Abstract

Background: Obstructive sleep apnea (OSA) is common in severely obese subjects (body mass index [BMI] > 35). Overnight polysomnography (OPS) is the “gold standard” method of evaluating this condition; however, it is time-consuming, inconvenient, and expensive. Selection of patients for OPS would be enhanced if we could better predict those likely to have clinically significant OSA.

Study objective: To look for clinical and biochemical predictors of OSA in symptomatic patients presenting for obesity surgery.

Design and patients: Symptoms suggestive of OSA were sought in a structured interview. We report OPS results of 99 consecutive subjects in whom OSA was clinically suspected. Predictors of apnea-hypopnea index (AHI) were sought from an extensive preoperative data collection. Multivariate linear and logistic analysis was used to identify independent predictors of AHI.

Results: Symptoms were poor predictors of AHI, with observed sleep apnea the only positive predictor. Four clinical and two biochemical factors independently predicted AHI: observed sleep apnea, male sex, higher BMI, age, fasting insulin, and glycosylated hemoglobin AIc (r2 = 0.42). Neck circumference (the best single measure) could replace BMI and sex in the analysis (r2 = 0.43). With cutoffs selected, a simple scoring system using these six factors provides a method of predicting those with moderate or severe OSA. A score ≥ 3 provides a sensitivity and specificity of 89% and 81%, and 96% and 71% for AHIs of ≥ 15 and ≥ 30, respectively. None of the 31 subjects with scores of 0 or 1 were found to have an AHI ≥ 15.

Conclusion: We explore sleep disturbance and report a simple method of predicting OSA in severely obese symptomatic subjects. This should assist in limiting the use of OPS to those with greater risk and provide a method of assessing risk in those not presenting primarily with a sleep problem.

Obesity is accompanied by considerable sleep disturbance and is the greatest risk factor for obstructive sleep apnea (OSA). Obesity increases the risk of OSA developing by approximately 10-fold from a range of 2 to 4% in the general adult population, to up to 20 to 40% in those with a body mass index (BMI) > 30.1–
Excessive daytime sleepiness is also seen in obese subjects and is not necessarily restricted to those with OSA. Several studies have now clearly demonstrated that excessive daytime sleepiness is related to obesity, but the cause, in the absence of sleep-disordered breathing, remains unclear.2–4

In a previous study,2
we demonstrated the very high prevalence of poor sleep quality, habitual snoring, observed sleep apnea, morning headaches, and excessive daytime somnolence in a severely obese population. This study also indicated that many subjects presenting to us with symptoms of significant sleep disturbance, especially women, had not had their sleep appropriately investigated with overnight polysomnography (OPS). While having limitations, OPS remains the “gold standard” for the assessment of sleep disorders where OSA is suspected.

OPS is time-consuming, expensive, often inconvenient, and a limited resource. It is therefore important to be able to better identify patients most likely to have significant OSA and thereby better focus the use of OPS. Studies have used a number of methods to predict OSA in at-risk groups including demographics,5
questionnaires,4
overnight pulse oximetry,6–
morphometry,7–
lung function utilizing flow-volume loop,8–
and MRI of the upper airways.9–
Increased fat distribution in the area of the upper airway and overnight pulse oximetry appear to be valuable predictive tools. Vgontzas et al10
found that hyperinsulinemia was associated with increased OSA and excessive daytime sleepiness in women with the polycystic ovary syndrome. This group has also reported an association between sleep apnea and visceral obesity, insulin resistance, and raised levels of cytokines associated with the metabolic syndrome.

This present study was designed to look for practical clinical and biochemical predictors of OSA in a group of symptomatic, severely obese patients who had not previously had their sleep problems investigated. This group was selected from patients presenting for bariatric surgery on the basis that they had symptoms suggestive of OSA.

Materials and Methods

Patients with a BMI > 35, with significant medical, physical, or psychosocial disabilities and who have attempted weight reduction by other means for at least 5 years, were considered for laparoscopic adjustable gastric band surgery. Preoperative evaluation prior included a structured interview with a specialist sleep physician (L.M.S.). A thorough sleep history was obtained, and OPS was arranged for all patients with a clinical indication based on symptoms suggestive of OSA. These symptoms were habitual snoring, observed sleep apnea, nocturnal choking, waking unrefreshed, morning headaches, excessive daytime sleepiness, and poor sleep quality. Patients were excluded from this study if they had any previous sleep study or management of any type for proven or suspected OSA.

In addition, all patients underwent an extensive preoperative assessment. This included an evaluation of medical history and current symptoms, with a major focus on obesity comorbidity, smoking, alcohol consumption, and medication usage. Anthropometric measurements including weight, height, neck circumference (measured at the level of the cricothyroid cartilage), waist, and hip measurements were obtained preoperatively. BP was recorded in the rested recumbent position on at least two occasions. In addition, fasting blood was obtained for lipid profile, plasma glucose, glycosylated hemoglobin AIc (HbAIc), plasma insulin, and liver function panel as markers of the metabolic syndrome. Lung function tests were performed on all patients preoperatively using a SensorMedics Vmax 22 system (SensorMedics; Yorba Linda, CA) and included lung volume, single-breath carbon monoxide diffusion, maximal inspiratory and expiratory pressures, and flow-loop spirometry.

Quality of Life

Health-related quality-of-life assessment is made using the Short-Form 36 Health Survey (SF-36), a widely used and validated questionnaire.13–14
The SF-36 is a multipurpose survey of general health status and outcomes. The responses to the 36 questions are grouped into eight general areas, and each is scaled from 0 to 100. These eight scaled scores are physical function, physical role, pain, general health, vitality, social functioning, emotional role, and mental health. Each scale score is weighted mathematically into physical and mental factors to provide the physical and mental component summary scores. The component summary scores give a measure of physical or mental ability and health status in a broad sense.

OPS

Each patient underwent full polysomnography performed in a sleep laboratory. Each study included the following recordings: EEG, chin and leg electromyography, electrooculography, chest and abdominal inductance plethysmography, airflow (nasal) via a thermistor, oxygen saturation, and heart rate monitoring. A trained sleep technician staged each study using the Rechtschaffen and Kales criteria15–
and scored each sleep study for arousals using the American Sleep Disorders Association criteria.16

Apneas were defined as complete cessation of airflow ≥ 10 s. Hypopneas were defined as reduction of > 50% in one of three respiratory signals—airflow signal or either respiratory or abdominal signals of respiratory inductance plethysmography, with an associated fall of ≥ 2% in oxygen saturation.

Patients were grouped by their total apnea hypopnea index (AHI) score, at polysomnography, for comparison between groups and ordinal regression analysis. These groups were AHI of 0 to 4.9 (normal), 5 to 14.9 (mild OSA), 15 to 29.9 (moderate OSA), and ≥ 30 (severe OSA), as used in other studies.17

All patients gave preoperative written informed consent to the procedure and follow-up requirements. The study was carried out in accordance with the Declaration of Helsinki.

Data Analysis

AHI was examined as a continuous (log[e]-transformed to attain a normal distribution), ordinal, and binary variable in correlation, multivariate analysis. Differences between two groups of quantitative variables were tested by two-sided unpaired Student t test (mean ± SD) or by Wilcoxon sign rank (median ± interquartile range) as appropriate. More than two groups were assessed for differences between them with analysis of variance (ANOVA) using the Tukey method for post hoc analysis (mean ± SD) and Kruskal-Wallis nonparametric test (median ± interquartile range). Some quantitative laboratory variables, eg, AHI, fasting plasma insulin, required log(e)-transformation prior to parametric analysis (median ± interquartile range). χ2 method (Fisher exact test) was used to test the significance of differences between proportions and categorical variables. The odds ratio (OR) was calculated for some 2 × 2 tables and expressed with the 95% confidence interval [CI]. Variables were assessed for correlation using the Pearson parametric and partial bivariate analysis. Multivariate analysis was tested using binary logistic regression (forward and backward), ordinal regression, and general linear regression and univariate general linear model analysis. Receiver operating characteristic (ROC) curves were used for assessing an appropriate cutoff for the continuous variables neck circumference, waist circumference, BMI, age, insulin, and HbAIc as predictors of AHI. These were also used to evaluate sensitivities, specificities, and area under the curve of the combination of variables affecting AHI. SPSS statistical software (SPSS; Chicago, IL) was used for statistical analysis.18
A p value < 0.05 was considered statistically significant. No correction was used for assessing for correlation with multiple variables.

Results

The study group consisted of 99 consecutive subjects, selected on the basis of presenting symptoms suggestive of significant sleep disturbance, who then underwent preoperative OPS. When compared with subjects not studied, selected subjects were more likely to be male (24% vs 14%, p = 0.001) and had a significantly higher BMI (47.8 ± 9.0 vs 43.9 ± 7.0, p < 0.001), but there was no age difference. Habitual snoring was observed in 94% and was unknown in the remainder, as sleep had not been observed. The percentage of patients with each symptom used as an indication for sleep study is shown in Table 1
. All subjects reported at least two symptoms. There were 18 subjects with two symptoms, 28 subjects with three symptoms, 22 patients with four symptoms, and 31 subjects with five or more symptoms. An additional indication for OPS was daytime oxygen saturation < 95%, but all 19 subjects had accompanying symptoms and none were selected on this basis alone. The characteristics of the 99 patients are shown in Table 2
.

Predictors of AHI

Observed sleep apnea was the only symptom predictive of the total AHI (as log[e]-transformed continuous, r2 = 0.13, p = 0.001; or ordinal variable, Cox and Snell pseudo-r2 = 0.11, p = 0.001) found at diagnostic OPS. No other symptoms were predictive of AHI. When the total numbers of symptoms were added, those complaining of greater numbers of symptoms had a higher AHI. However, after controlling for observed sleep apnea, this total symptom score was no longer predictive. Habitual snoring was reported in almost all patients who were referred for OPS; therefore, its ability to predict AHI could not be assessed.

Subjects were grouped by the presence and severity of sleep apnea, with characteristics of the groups and differences between groups shown in Table 2
. Using ordinal logistic regression, observed sleep apnea, older age, male sex, and BMI were all independent predictors of the presence and severity of OSA (Cox and Snell r2 = 0.35). When measures of weight distribution were added to the model, neck circumference replaced BMI and sex and, with age and observed sleep apnea, were independent predictors of OSA (Cox and Snell r2 = 0.37). Log(e)-transformation total AHI provided a normally distributed variable. Linear regression analysis showed the three variables of neck circumference (β = 0.39, p < 0.001), age (β = 0.24, p = 0.010), and observed sleep apnea (β = 0.23, p < 0.017) provided the best clinical predictors of log(e) AHI with a combined r2 = 0.33. If neck circumference was replaced by sex and BMI in the model with age and observed sleep apnea, the combined group was a significant predictor with a combined r2 = 0.32.

Significant sleep apnea was considered to be an AHI ≥ 15. Using binary logistic analysis, the same variables described above were found to be independent predictors of significant sleep apnea. ROC curves were used to determine cutoff values for continuous predictive variables. A neck circumference ≥ 43 cm, age ≥ 38 years, and BMI ≥ 45 provided the best balance of sensitivity and specificity. The ORs for each of the predictive variables before and after adjustment for one another are shown in Table 3
.

Of the biochemical measures, raised fasting plasma glucose, plasma insulin, HbAIc, and low high-density lipoprotein (HDL) cholesterol were all significantly associated with raised AHI. When combined with the three clinical features, raised HbAIc and fasting plasma insulin remained significant additional predictors of higher AHI (r2 = 0.41, p < 0.001). When neck circumference was replaced with the combination sex and BMI, the four clinical and two laboratory predictors had independent effects (r2 = 0.42, p < 0.001). Using ROC curves, an HbAIc of 6.0% and fasting insulin concentration of 28 μmol/L were selected to give the best combination of sensitivity and specificity. HbAIc levels of ≥ 6% and fasting plasma insulin concentration of ≥ 28 μmol/L were associated with ORs of 5.9 (95% CI, 2.2 to 15.8) and 11.4 (95% CI, 3.6 to 36), respectively, for an AHI ≥ 15. Using binary logistic regression, six independent binary factors are predictive of AHI ≥ 15 (Cox and Snell r2 = 0.46). These are BASH’IM (BMI ≥ 45, age, observed sleep apnea, HbAIc ≥ 6%, fasting plasma insulin ≥ 28 μmol/L, and male sex), an acronym reflecting the sleeping partner’s common reaction to the problem. The same six measures were independent predictors of AHI ≥ 30 (Cox and Snell r2 = 0.48). A score of 1 for each of these six factors totaled provides a simple way of combining risk to produce a BASH’IM score. These factors could have been differentially weighted according to the corrected ORs in Table 3
; however, this would be complicated in the clinical setting. Table 4
shows the number of subjects with each score and the sensitivity and specificity for predicting AHIs ≥ 15 and ≥ 30. A combined score of ≥ 3 of the six predictors had an OR of 34 (95% CI, 10 to 114), sensitivity of 89%, and specificity of 81% for AHI ≥ 15 and OR of 54 (95% CI, 7 to 400), 96% and 71%, respectively, for an AHI ≥ 30. All patients with an AHI ≥ 15 had a score of at least 2. The use of neck circumference in place of sex and BMI had similar predictive values with the same area under the ROC curve as the six variables.

Neck and Waist Circumference

Neck circumference was the best and waist circumference was the second best single clinical measures for predicting AHI. A neck circumference ≥ 43 cm and waist circumference ≥ 126 cm provided the best combination of sensitivity and specificity with values of 67% and 83%, and 74% and 63%, respectively, in women. In the clinical setting, either measure above these levels would imply significant risk. There were too few men (n = 24) to assess for appropriate cutoffs when analyzed alone.

In multivariate analysis, with all subjects included, neck circumference was reliably a better predictor. With neck circumference ≥ 43 cm (with a score of 0 or 2) replacing BMI and male sex in the BASH’IM score, five factors are independently predictors of AHI: neck circumference ≥ 43 cm, age ≥ 38 years, observed sleep apnea, HbAIc ≥ 6%, and fasting plasma insulin concentration ≥ 28 μmol/L. A score ≥ 3 of the five predictors (possible scores, 1 to 6) had a sensitivity of 80% and specificity of 91% for AHI ≥ 15, and 86% and 84%, respectively, for AHI ≥ 30. The combination of five variables provided the same areas under the ROC curves for predicting AHI as the six combined for BASH’IM. The area of under the curve for predicting AHI ≥ 15 was 0.91 for both, and for predicting AHI ≥ 30 was 0.92 for both.

Quality of Life

There was no association between AHI, as a continuous, ordinal, or binary variable, and measures of health-related quality of life as measured by the SF-36. The group selected for sleep studies did have a significantly lower physical component summary score than those not studied by OPS, but this difference was not significant after controlling for differences in BMI and sex between groups.

Lung Function Studies

Lung function tests including lung volume, single-breath carbon monoxide diffusion, maximal inspiratory and expiratory pressures, and flow volume-loop spirometry were performed preoperatively. After controlling for sex, age, smoking, and BMI, there was no association between AHI as a continuous, ordinal, or binary variable, and any of the lung function studies. There was also no association between AHI and random daytime pulse oximetry.

Excessive Day Sleepiness—ESS

Excessive daytime sleepiness as measured with the ESS score was not a predictor of AHI when analyzed as a continuous, ordinal, or binary variable. An abnormal ESS score > 10 was not a predictor of AHI ≥ 15 or ≥ 30. When the ESS was used as the dependent variable, there were no demographic, weight, weight distribution, biochemical, or polysomnographic variables that were associated with a higher ESS score. However, the group of obese patients did have a high mean ESS score of 10.1 ± 5.5 compared with a high mean ESS score of 4 ± 3 for the community norm.11

Sleep Changes With Increasing BMI

Increasing BMI was associated with a number of polysomnographic changes after controlling for age and sex. Higher BMI was associated with reduced sleep efficiency (r = − 0.36, p < 0.001). Higher BMI was associated with higher AHI (r = 0.28, p = 0.01) and significantly lower percentage of rapid eye movement (REM) sleep (r = − 0.35, p = 0.003). After controlling for AHI, the effect of increasing BMI on the reduced proportion of REM sleep remained significant (r = − 0.29, p = 0.01). The reduced portion of REM sleep was best predicted by increased BMI, with measures of neck, waist, hip, or waist to hip ratio of no added predictive value. There was no significant correlation between neck circumference or total AHI and the portion of REM sleep. The percentage of REM sleep was reduced with a raised arousal index (β = − 0.27, p = 0.04) in addition to a raised BMI (β = − 0.28, p = 0.03) with a combined r2 of 0.19 (p = 0.003). None of the biochemical measures predicted the reduced proportion of REM sleep. Unadjusted sleep data for subjects grouped into quartiles by their preoperative BMI are shown in Table 5
.

Discussion

This study of symptomatic severely obese patients has shown several simple clinical measures that predict the presence and severity of OSA. In addition to older age, male sex, and observed sleep apnea, measures of increased obesity, especially central or upper body obesity, increase the risk of higher AHI. We have found that neck circumference is the best simple clinical measure of increased risk within the whole group and in women analyzed separately, confirming the importance of neck circumference as a predictor of OSA.19–22
This finding suggests that fat distribution in the area of the upper airway may be an important etiologic factor in the development of OSA.

Central and upper body distribution of body fat are features of the metabolic syndrome. We demonstrate that hyperinsulinemia, raised HbAIc, and low HDL cholesterol, all features of this syndrome, are associated with higher AHI in this study. Two of these factors, fasting plasma insulin and HbAIc, are independently predictive of sleep apnea after controlling for other significant factors including BMI and neck circumference. Our findings are consistent with those of Vgontzas et al,23–
Strohl et al,24–
and Grunstein et al,25–
and contrast with those of Stoohs et al,26
who found that any association between insulin resistance and sleep apnea in women was lost after controlling for BMI. The association with the metabolic syndrome may imply metabolic factors in the etiology of OSA or may indicate more subtle local fat distribution around the upper airways in association with hyperinsulinemia and insulin resistance. Putative metabolic factors may include insulin resistance-associated cytokines, tumor necrosis factor-α, or interleukin 6.23,27–28
OSA may well be considered a sleep manifestation of the metabolic syndrome.25,29–
The importance of looking for OSA in those with other manifestations of the syndrome should not be overlooked. Further, we have demonstrated an association between larger neck circumference, raised free-androgen index, and hyperinsulinemia in premenopausal women with the polycystic ovary syndrome,30–
which is increasingly recognized as the ovarian manifestation of the metabolic syndrome.31
Using our predictors, this would imply that women with this syndrome would be at high risk of OSA. Indeed, recent studies10,32
have shown a high risk of OSA in women with polycystic ovary syndrome.

Our study has found independent clinical predictors of significant sleep apnea. Combining these indicators by simply adding the number of positive predictive factors provides a BASH’IM score of 0 to 6. Any severely obese patient with a score ≥ 3 is at very high risk of significant OSA. For a score of 0 or 1, the risk would be very low, with no cases found in the current study. One could argue that in this group, polysomnography is not indicated. If polysomnography had not been performed on those with a score of 0 or 1, then 49% of negative study findings would have been avoided. If neck circumference (score 0 to 2) replaces sex and BMI, a similar interpretation of scoring applies. Finding significant predictors in this group of symptomatic severely obese patients allows us to better select patients for OPS.

The only symptom with predictive value of AHI was observed sleep apnea. Habitual snoring was almost a universal symptom and thus was not of value in discrimination. The finding of poor correlation between reported symptoms and AHI is clinically important and has been reported previously.21,33
While our findings need to be validated prospectively, the apparent limited predictive value of symptoms should alert us to the possibility of asymptomatic obese patients with high BASH’IM score where OPS should be considered. As we only studied selected symptomatic patients, thought to be at risk of OSA, the importance of some symptoms may have been underestimated.

Daytime sleepiness as measured by the ESS was not associated with sleep apnea in these obese subjects with high scores occurring in both those with and without sleep apnea. We have previously shown raised ESS scores in severely obese subjects, with no association with observed sleep apnea.2–
The finding of excessive daytime sleepiness in the absence of OSA in the obese population has now been reported consistently.3–4
We have not found any major predictors of a high ESS in this severely obese group. No measures of weight distribution or glucose metabolism biomarkers significantly correlated with ESS scoring. We did not use the multiple sleep latency test, which has been considered by some to be the “gold standard” test for daytime sleepiness. Several studies have shown a poor correlation between multiple sleep latency and ESS34–35
and neither correlate well with AHI.36
Perhaps they are measuring different things.34,37
The cause of excessive daytime sleepiness in severely obese patients is unclear. It is clear, however, that it improves substantially with weight loss.2

The ability of lung function tests to predict OSA has been variously reported. We have not found extensive lung function to be predictive after controlling for age, sex, smoking, and BMI. Our findings are consistent with others.8,33
However, Herer et al38
reported that an index of upper airways obstruction, the ratio of forced expiratory volume in 0.5 to FEV1, is a significant but minor predictor of OSA. We did not measure this ratio. Some difficulty is experienced when assessing lung function in severely obese patients, as normative values are unreliable.

In these obese patients, we confirmed that increasing BMI was associated with reduced sleep efficiency and a reduction in the proportion of REM sleep.39
However, the reduction REM sleep did not correlate with increased AHI, excessive daytime sleepiness, or reported sleep quality. It is important to stress that our findings are based on a selected obese population; therefore, caution is needed before generalizing the results to other settings.

Conclusion

We have reported simple clinical and biochemical predictors of OSA in severely obese subjects. These predictors may assist in limiting the use of OPS in patients with symptoms to those with greater risk, and provide a method of better assessing risk in those not presenting primarily with a sleep problem. The presence of excessive daytime sleepiness is common in severely obese subjects, but is not related to the presence or severity of OSA.

Johns, MW Sensitivity and specificity of the multiple sleep latency test (MSLT), the maintenance of wakefulness test and the Epworth sleepiness scale: failure of the MSLT as a gold standard.J Sleep Res2000;9,5-11. [PubMed]

Johns, MW Sensitivity and specificity of the multiple sleep latency test (MSLT), the maintenance of wakefulness test and the Epworth sleepiness scale: failure of the MSLT as a gold standard.J Sleep Res2000;9,5-11. [PubMed]

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